RAG & MCP

RAG Evaluation

Evaluate RAG system quality: retrieval precision/recall, answer faithfulness, and end-to-end pipeline benchmarking.

Last updated 2026-06-11

RAG Evaluation — Measuring Every Layer

The key principle: evaluate each component independently so you can assign blame when quality drops. Don't just measure end-to-end — you'll never know whether to fix retrieval, generation, or chunking.

Evaluation at Every Stage

WHAT TO EVALUATE AT EACH STAGE

1 INGESTION / CHUNKING QUALITY

RAG Architecture

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Retrieval-Augmented Generation (RAG) architecture explained: ingestion pipeline, vector search, prompt augmentation, and production patterns.

Document Processing

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Document processing for RAG pipelines: PDF parsing, OCR, table extraction, and multi-modal document understanding.

Chunking Strategies

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Text chunking strategies for RAG: fixed-size, semantic, recursive, and document-aware chunking with performance comparisons.

Embedding & Indexing

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Embedding models and vector indexing for RAG: choosing embeddings, HNSW vs IVF, dimensionality, and index optimization.

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RAG Evaluation — Measuring Every Layer

RAG Evaluation — Measuring Every Layer

Evaluation at Every Stage

More in RAG & MCP

RAG Architecture

Document Processing

Chunking Strategies

Embedding & Indexing